Supply chains face many challenges as competitive markets force manufacturers to deliver products not just quickly, but also more precisely. This challenge is compounded by ever-increasing complexity, with many interrelated components, from material input through manufacturing and distribution to customer purchase, making it difficult for manufacturers to manage them effectively. As supply chain complexity grows, many legacy approaches fall short when it comes to providing sufficient visibility into the performance of individual business processes or connecting those details back up the supply chain’s value stream.
The result is that manufacturers often do not have an efficient method for measuring the overall health and resiliency of their supply chains at any given moment, which makes it challenging for them to respond to changing market conditions and customer demands. Additionally, there may be several different business units within a given enterprise that serve as stakeholders to the operation of an individual supply chain.
To maintain resiliency and increase operational visibility in a world of accelerating change, manufacturers need to ensure their supply chains are as flexible and agile as possible – and that means taking steps to proactively anticipate potential disruptions. This can be achieved by leveraging Microsoft Azure’s predictive capabilities, which many manufacturers find to be more accurate and insightful than traditional methods of managing supply chains.
Utilizing Azure for supply chain management
Azure offers its customers a unique platform that gathers existing data from several disparate systems so manufacturers can analyze item sales trends and predict inventory needs based on those trends. It then utilizes advanced techniques to forecast demand for days, weeks, and months into the future. This gives companies time to react proactively to any supply chain issues before they impact production output and customer satisfaction through downtime in their operations.
Azure users can benefit with insights from big data analytics using a rich set of analytical functions such as forecasting, pattern recognition, and predictive analysis. This allows organizations to move beyond descriptive analytics by helping predict future outcomes based on historical performance or identify potential service disruptions before they occur. By predicting customer churn or identifying anomalies in IoT devices, for example, it is possible to adjust operations accordingly and potentially reduce costs while strengthening customer relationships.
Machine learning and IoT: The perfect combination for business success
Azure offers predictive capabilities that helps recommend changes to an organization’s supply chain in real-time based on what they’ve learned from past successes or previous failures. Traditional supply chain management systems might struggle incorporating legacy data into their analytics due to disparate data sources, but machine learning can still work with inconsistent data. Machine learning can also help reduce the cost of collecting data by identifying patterns and trends that might not otherwise be readily apparent or available, enabling companies to make better decisions more quickly.
But how do companies get started analyzing big data in the cloud using open standards such as Apache Hadoop and Apache Spark? Azure gives users access to both batch processing for large file sizes and analytics for smaller data sets that can be processed in real-time. It also offers a broad portfolio of open-source tools to help organizations derive insights from their data, such as Azure HDInsight, Azure Stream Analytics, and R Server for Hadoop on HDInsight. Using these tools, companies are able to leverage not only their data but also the larger datasets available from third-party vendors and data found in the public domain.
In addition to having access to an extensive toolkit with which they can run complex analytical jobs, Azure users can benefit from the high resiliency of the underlying infrastructure and pay only for the resources they use. Organizations can also take advantage of the latest distributed storage technology; Azure HDInsight is a service that delivers managed Apache Hadoop in the cloud with open-source tools. Users get an enterprise-grade, highly scalable, and secure Hadoop solution. With the right support and partnership, companies can benefit from enhanced security, reliability, performance, and compatibility.
Gain unprecedented insights and drive profits with Azure for IoT predictive analytics
It is vital to keep the right amount of stock on hand to be able to meet the consumer demand while reducing day of inventory on hand. Companies can optimize their supply chain by replacing costly and time-consuming physical inspections with information emerging from real-time sensor data, machine learning algorithms, and IoT cloud applications. Organizations need systems that enable them to get an overview of all components leaving and entering the production line or warehouse in real-time.
Azure IoT Central manages massive amounts of device telemetry, providing organizations with operational insights about assets and processes across manufacturing lines, warehouses, oil rigs, farms and more. Monitoring and managing machines in real-time means being able to spot problems before they become critical issues. Azure IoT Suite offers Predictive Maintenance offering a system that will learn the norm of the machine or plant and use predictive analytics to predict when it is going to break.
Azure IoT Edge, powered by Microsoft Azure Stack, connects devices running anywhere with intelligence at the edge: in the factory on any device – from an assembly line to classifying images of Mars. Using edge capabilities such as AI and computer vision, it can coordinate complex operations across multiple entities without sending data back to the Cloud for processing, which reduces latencies and optimizes performance. In addition, it enables new ways to generate revenue by connecting physical assets with cloud services. For example, retailers can keep track of their shelves via video feed streams from cameras installed in storerooms and shipping docks; using advanced image recognition capabilities to quickly identify discrepancies and allow for rapid adjustments.
The bottom line
To better understand business risk and improve supply chain management, companies need to analyze multiple data sources in their production operations, everything from industrial sensors to manufacturing equipment. The data can be processed at scale in the cloud with Azure predictive capabilities that help extract critical insights. This is a significant step up from earlier approaches that relied on aggregated or summarized views of raw data captured by sensors and equipment.
If you’re interested in deploying Azure in your supply chain infrastructure, feel free to contact the experts at Hanu today.